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      <div class="title">prtScoreRocNfa</div>
      <div class="helptext"><pre><!--helptext -->  <span class="helptopic">prtScoreRocNfa</span>   Generate a operator characteristic curve and output the number of false alarms
 
     <span class="helptopic">prtScoreRocNfa</span>(DECSTATS,LABELS) plots the receiver operator
     characteristic curve for the decision statistics DECSTATS and the
     corresponding labels LABELS. DECSTATS must be a Nx1 vector of decision
     statistics. LABELS must be a Nx1 vector of binary class labels. This
     behavior is the same as prtScoreRoc.
 
     [NF,PD,THRESHOLDS,AUC] = <span class="helptopic">prtScoreRocNfa</span>(DECSTATS,LABELS) outputs the
     number of false alarms NF, the probability of detection PD, the
     thresholds used to obtain each PD and NF pair, and the area under the
     ROC scurve AUC.
 
     Example:     
     TestDataSet = prtDataGenSpiral;       % Create some test and
     TrainingDataSet = prtDataGenSpiral;   % training data
     classifier = prtClassSvm;             % Create a classifier
     classifier = classifier.train(TrainingDataSet);    % Train
     classified = run(classifier, TestDataSet);     
     % Find the number of false alarms at the corresponding PD.
     [nf, pd ]= <span class="helptopic">prtScoreRocNfa</span>(classified.getX, TestDataSet.getY);</pre></div><!--after help --><!--seeAlso--><div class="footerlinktitle">See also</div><div class="footerlink"> <a href="./prtScoreConfusionMatrix.html">prtScoreConfusionMatrix</a>, <a href="./prtScoreRmse.html">prtScoreRmse</a>,
    <a href="./prtScoreRocBayesianBootstrap.html">prtScoreRocBayesianBootstrap</a>, <a href="./prtScoreRocBayesianBootstrapNfa.html">prtScoreRocBayesianBootstrapNfa</a>,
    <a href="./prtScorePercentCorrect.html">prtScorePercentCorrect</a>
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